As described in Section REF , these probabilistic approaches detect anomalies by estimating the normal data density function. For example, Breunig et al. [1]} proposed a method referred to as Local Outlier Factor (LOF) to detect anomalies by computing the local density. Tang et al. [2]} calculate the local connectivity for determining anomalies instead. In [3]} and [4]}, a Gaussian Mixture Model (GMM) and Kernel Density Estimation (KDE) are respectively used for estimating the density of normal representations.
Over the last years, efforts have been made to introduce DNN-based probabilistic methods. For instance, Zong et al. [5]} proposed to train an auto-encoder for extracting relevant representations before fitting a GMM. Nevertheless, as for clustering-based methods, probabilistic approaches usually do not model the temporal aspect restricting their effectiveness in the context of time-series anomaly detection.
